Overview

Dataset statistics

Number of variables26
Number of observations201
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory145.4 KiB
Average record size in memory740.9 B

Variable types

NUM16
CAT10

Reproduction

Analysis started2021-06-23 17:37:11.550352
Analysis finished2021-06-23 17:38:55.233746
Duration1 minute and 43.68 seconds
Versionpandas-profiling v2.7.1
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
highway_mpg is highly correlated with city_mpgHigh correlation
city_mpg is highly correlated with highway_mpgHigh correlation
fuel_system is highly correlated with fuel_typeHigh correlation
fuel_type is highly correlated with fuel_systemHigh correlation
symboling has 65 (32.3%) zeros Zeros

Variables

symboling
Real number (ℝ)

ZEROS
Distinct count6
Unique (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8407960199004975
Minimum-2
Maximum3
Zeros65
Zeros (%)32.3%
Memory size1.7 KiB
2021-06-23T10:38:55.523896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.254801723
Coefficient of variation (CV)1.492397315
Kurtosis-0.7071776172
Mean0.8407960199
Median Absolute Deviation (MAD)1
Skewness0.1973703603
Sum169
Variance1.574527363
2021-06-23T10:38:55.851893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 65 32.3%
 
1 52 25.9%
 
2 32 15.9%
 
3 27 13.4%
 
-1 22 10.9%
 
-2 3 1.5%
 
ValueCountFrequency (%) 
-2 3 1.5%
 
-1 22 10.9%
 
0 65 32.3%
 
1 52 25.9%
 
2 32 15.9%
 
ValueCountFrequency (%) 
3 27 13.4%
 
2 32 15.9%
 
1 52 25.9%
 
0 65 32.3%
 
-1 22 10.9%
 

normalized_losses
Real number (ℝ≥0)

Distinct count58
Unique (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.18905472636816
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:38:56.186893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile77
Q1101
median122
Q3150
95-th percentile186
Maximum256
Range191
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.5729665
Coefficient of variation (CV)0.2681781293
Kurtosis0.4174688895
Mean125.1890547
Median Absolute Deviation (MAD)27
Skewness0.5513515775
Sum25163
Variance1127.14408
2021-06-23T10:38:56.529891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
161 16 8.0%
 
168 9 4.5%
 
128 9 4.5%
 
91 8 4.0%
 
150 7 3.5%
 
104 6 3.0%
 
134 6 3.0%
 
102 5 2.5%
 
115 5 2.5%
 
103 5 2.5%
 
Other values (48) 125 62.2%
 
ValueCountFrequency (%) 
65 5 2.5%
 
74 5 2.5%
 
77 1 0.5%
 
78 1 0.5%
 
81 2 1.0%
 
ValueCountFrequency (%) 
256 1 0.5%
 
231 1 0.5%
 
197 2 1.0%
 
194 2 1.0%
 
192 2 1.0%
 

make
Categorical

Distinct count22
Unique (%)10.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota
32
nissan
 
18
mazda
 
17
honda
 
13
mitsubishi
 
13
Other values (17)
108
ValueCountFrequency (%) 
toyota 32 15.9%
 
nissan 18 9.0%
 
mazda 17 8.5%
 
honda 13 6.5%
 
mitsubishi 13 6.5%
 
subaru 12 6.0%
 
volkswagen 12 6.0%
 
volvo 11 5.5%
 
peugot 11 5.5%
 
dodge 9 4.5%
 
Other values (12) 53 26.4%
 
2021-06-23T10:38:56.917893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length13
Mean length6.502487562
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 24 96.0%
 
Dash_Punctuation 1 4.0%
 
ValueCountFrequency (%) 
Latin 24 96.0%
 
Common 1 4.0%
 
ValueCountFrequency (%) 
ASCII 25 100.0%
 

fuel_type
Categorical

HIGH CORRELATION
Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
gas
181
diesel
 
20
ValueCountFrequency (%) 
gas 181 90.0%
 
diesel 20 10.0%
 
2021-06-23T10:38:57.288326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length6
Mean length3.298507463
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 7 100.0%
 
ValueCountFrequency (%) 
Latin 7 100.0%
 
ValueCountFrequency (%) 
ASCII 7 100.0%
 

aspiration
Categorical

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
std
165
turbo
36
ValueCountFrequency (%) 
std 165 82.1%
 
turbo 36 17.9%
 
2021-06-23T10:38:57.695325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length5
Mean length3.358208955
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 7 100.0%
 
ValueCountFrequency (%) 
Latin 7 100.0%
 
ValueCountFrequency (%) 
ASCII 7 100.0%
 

number_of_doors
Categorical

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
114
two
87
ValueCountFrequency (%) 
four 114 56.7%
 
two 87 43.3%
 
2021-06-23T10:38:58.072324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length4
Mean length3.567164179
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 6 100.0%
 
ValueCountFrequency (%) 
Latin 6 100.0%
 
ValueCountFrequency (%) 
ASCII 6 100.0%
 

body_style
Categorical

Distinct count5
Unique (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
sedan
94
hatchback
68
wagon
25
hardtop
 
8
convertible
 
6
ValueCountFrequency (%) 
sedan 94 46.8%
 
hatchback 68 33.8%
 
wagon 25 12.4%
 
hardtop 8 4.0%
 
convertible 6 3.0%
 
2021-06-23T10:38:58.462328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length11
Mean length6.611940299
Min length5
ValueCountFrequency (%) 
Lowercase_Letter 18 100.0%
 
ValueCountFrequency (%) 
Latin 18 100.0%
 
ValueCountFrequency (%) 
ASCII 18 100.0%
 

drive_wheels
Categorical

Distinct count3
Unique (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
fwd
118
rwd
75
4wd
 
8
ValueCountFrequency (%) 
fwd 118 58.7%
 
rwd 75 37.3%
 
4wd 8 4.0%
 
2021-06-23T10:38:58.848038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length3
Mean length3
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 4 80.0%
 
Decimal_Number 1 20.0%
 
ValueCountFrequency (%) 
Latin 4 80.0%
 
Common 1 20.0%
 
ValueCountFrequency (%) 
ASCII 5 100.0%
 

engine_location
Categorical

Distinct count2
Unique (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
front
198
rear
 
3
ValueCountFrequency (%) 
front 198 98.5%
 
rear 3 1.5%
 
2021-06-23T10:38:59.800257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length5
Mean length4.985074627
Min length4
ValueCountFrequency (%) 
Lowercase_Letter 7 100.0%
 
ValueCountFrequency (%) 
Latin 7 100.0%
 
ValueCountFrequency (%) 
ASCII 7 100.0%
 

wheel_base
Real number (ℝ≥0)

Distinct count52
Unique (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.79701492537313
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:00.139259image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile93
Q194.5
median97
Q3102.4
95-th percentile110
Maximum120.9
Range34.3
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation6.066365555
Coefficient of variation (CV)0.06140231625
Kurtosis0.9484450961
Mean98.79701493
Median Absolute Deviation (MAD)2.8
Skewness1.031261443
Sum19858.2
Variance36.80079104
2021-06-23T10:39:00.511261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
93.7 20 10.0%
 
94.5 19 9.5%
 
95.7 13 6.5%
 
96.5 8 4.0%
 
97.3 7 3.5%
 
100.4 6 3.0%
 
104.3 6 3.0%
 
96.3 6 3.0%
 
98.8 6 3.0%
 
98.4 6 3.0%
 
Other values (42) 104 51.7%
 
ValueCountFrequency (%) 
86.6 2 1.0%
 
88.4 1 0.5%
 
88.6 2 1.0%
 
89.5 3 1.5%
 
91.3 2 1.0%
 
ValueCountFrequency (%) 
120.9 1 0.5%
 
115.6 2 1.0%
 
114.2 4 2.0%
 
113 2 1.0%
 
112 1 0.5%
 

length
Real number (ℝ≥0)

Distinct count73
Unique (%)36.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.20099502487562
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:00.850260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.3
Q1166.8
median173.2
Q3183.5
95-th percentile197
Maximum208.1
Range67
Interquartile range (IQR)16.7

Descriptive statistics

Standard deviation12.32217509
Coefficient of variation (CV)0.07073538868
Kurtosis-0.06519162777
Mean174.200995
Median Absolute Deviation (MAD)6.9
Skewness0.1544463518
Sum35014.4
Variance151.835999
2021-06-23T10:39:01.200466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
157.3 15 7.5%
 
188.8 11 5.5%
 
186.7 7 3.5%
 
166.3 7 3.5%
 
171.7 7 3.5%
 
186.6 6 3.0%
 
165.3 6 3.0%
 
176.2 6 3.0%
 
177.8 6 3.0%
 
176.8 5 2.5%
 
Other values (63) 125 62.2%
 
ValueCountFrequency (%) 
141.1 1 0.5%
 
144.6 2 1.0%
 
150 3 1.5%
 
155.9 1 0.5%
 
156.9 1 0.5%
 
ValueCountFrequency (%) 
208.1 1 0.5%
 
202.6 2 1.0%
 
199.6 2 1.0%
 
199.2 1 0.5%
 
198.9 4 2.0%
 

width
Real number (ℝ≥0)

Distinct count43
Unique (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.88905472636816
Minimum60.3
Maximum72.0
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:01.552868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164.1
median65.5
Q366.6
95-th percentile70.3
Maximum72
Range11.7
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.101470819
Coefficient of variation (CV)0.03189408055
Kurtosis0.6786551692
Mean65.88905473
Median Absolute Deviation (MAD)1.4
Skewness0.8750290419
Sum13243.7
Variance4.416179602
2021-06-23T10:39:01.881869image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
63.8 24 11.9%
 
66.5 23 11.4%
 
65.4 15 7.5%
 
68.4 10 5.0%
 
64.4 10 5.0%
 
63.6 9 4.5%
 
64 9 4.5%
 
65.5 8 4.0%
 
65.2 7 3.5%
 
64.2 6 3.0%
 
Other values (33) 80 39.8%
 
ValueCountFrequency (%) 
60.3 1 0.5%
 
61.8 1 0.5%
 
62.5 1 0.5%
 
63.4 1 0.5%
 
63.6 9 4.5%
 
ValueCountFrequency (%) 
72 1 0.5%
 
71.7 3 1.5%
 
71.4 3 1.5%
 
70.9 1 0.5%
 
70.6 1 0.5%
 

height
Real number (ℝ≥0)

Distinct count49
Unique (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.766666666666666
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:02.243867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.5
95-th percentile57.5
Maximum59.8
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.447822161
Coefficient of variation (CV)0.04552676059
Kurtosis-0.4329081504
Mean53.76666667
Median Absolute Deviation (MAD)1.6
Skewness0.02917329915
Sum10807.1
Variance5.991833333
2021-06-23T10:39:02.624871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
50.8 14 7.0%
 
55.7 12 6.0%
 
54.5 10 5.0%
 
54.1 10 5.0%
 
52 9 4.5%
 
55.5 9 4.5%
 
56.7 8 4.0%
 
54.3 8 4.0%
 
52.6 7 3.5%
 
56.1 7 3.5%
 
Other values (39) 107 53.2%
 
ValueCountFrequency (%) 
47.8 1 0.5%
 
48.8 2 1.0%
 
49.4 2 1.0%
 
49.6 4 2.0%
 
49.7 3 1.5%
 
ValueCountFrequency (%) 
59.8 2 1.0%
 
59.1 3 1.5%
 
58.7 4 2.0%
 
58.3 1 0.5%
 
57.5 3 1.5%
 

curb_weight
Real number (ℝ≥0)

Distinct count169
Unique (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2555.6666666666665
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:02.952870image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1905
Q12169
median2414
Q32926
95-th percentile3505
Maximum4066
Range2578
Interquartile range (IQR)757

Descriptive statistics

Standard deviation517.2967266
Coefficient of variation (CV)0.2024116577
Kurtosis0.03491557605
Mean2555.666667
Median Absolute Deviation (MAD)377
Skewness0.7058035875
Sum513689
Variance267595.9033
2021-06-23T10:39:03.297655image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2385 4 2.0%
 
1989 3 1.5%
 
2275 3 1.5%
 
1918 3 1.5%
 
2756 2 1.0%
 
2414 2 1.0%
 
2403 2 1.0%
 
4066 2 1.0%
 
2145 2 1.0%
 
2337 2 1.0%
 
Other values (159) 176 87.6%
 
ValueCountFrequency (%) 
1488 1 0.5%
 
1713 1 0.5%
 
1819 1 0.5%
 
1837 1 0.5%
 
1874 1 0.5%
 
ValueCountFrequency (%) 
4066 2 1.0%
 
3950 1 0.5%
 
3900 1 0.5%
 
3770 1 0.5%
 
3750 1 0.5%
 

engine_type
Categorical

Distinct count6
Unique (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
ohc
145
ohcf
 
15
ohcv
 
13
dohc
 
12
l
 
12
ValueCountFrequency (%) 
ohc 145 72.1%
 
ohcf 15 7.5%
 
ohcv 13 6.5%
 
dohc 12 6.0%
 
l 12 6.0%
 
rotor 4 2.0%
 
2021-06-23T10:39:03.675653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length5
Mean length3.119402985
Min length1
ValueCountFrequency (%) 
Lowercase_Letter 9 100.0%
 
ValueCountFrequency (%) 
Latin 9 100.0%
 
ValueCountFrequency (%) 
ASCII 9 100.0%
 
Distinct count7
Unique (%)3.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
four
157
six
 
24
five
 
10
two
 
4
eight
 
4
Other values (2)
 
2
ValueCountFrequency (%) 
four 157 78.1%
 
six 24 11.9%
 
five 10 5.0%
 
two 4 2.0%
 
eight 4 2.0%
 
twelve 1 0.5%
 
three 1 0.5%
 
2021-06-23T10:39:04.087500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length6
Mean length3.895522388
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 14 100.0%
 
ValueCountFrequency (%) 
Latin 14 100.0%
 
ValueCountFrequency (%) 
ASCII 14 100.0%
 

engine_size
Real number (ℝ≥0)

Distinct count43
Unique (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.87562189054727
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:04.422502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q198
median120
Q3141
95-th percentile194
Maximum326
Range265
Interquartile range (IQR)43

Descriptive statistics

Standard deviation41.54683445
Coefficient of variation (CV)0.3274611295
Kurtosis5.497490767
Mean126.8756219
Median Absolute Deviation (MAD)22
Skewness1.979144197
Sum25502
Variance1726.139453
2021-06-23T10:39:04.760501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
122 15 7.5%
 
92 15 7.5%
 
97 14 7.0%
 
98 14 7.0%
 
108 13 6.5%
 
110 12 6.0%
 
90 10 5.0%
 
109 8 4.0%
 
141 7 3.5%
 
120 7 3.5%
 
Other values (33) 86 42.8%
 
ValueCountFrequency (%) 
61 1 0.5%
 
70 3 1.5%
 
79 1 0.5%
 
80 1 0.5%
 
90 10 5.0%
 
ValueCountFrequency (%) 
326 1 0.5%
 
308 1 0.5%
 
304 1 0.5%
 
258 2 1.0%
 
234 2 1.0%
 

fuel_system
Categorical

HIGH CORRELATION
Distinct count8
Unique (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
mpfi
92
2bbl
64
idi
20
1bbl
 
11
spdi
 
9
Other values (3)
 
5
ValueCountFrequency (%) 
mpfi 92 45.8%
 
2bbl 64 31.8%
 
idi 20 10.0%
 
1bbl 11 5.5%
 
spdi 9 4.5%
 
4bbl 3 1.5%
 
spfi 1 0.5%
 
mfi 1 0.5%
 
2021-06-23T10:39:05.134492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Length

Max length4
Mean length3.895522388
Min length3
ValueCountFrequency (%) 
Lowercase_Letter 8 72.7%
 
Decimal_Number 3 27.3%
 
ValueCountFrequency (%) 
Latin 8 72.7%
 
Common 3 27.3%
 
ValueCountFrequency (%) 
ASCII 11 100.0%
 

bore
Real number (ℝ≥0)

Distinct count39
Unique (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.329701492537313
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:05.473145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.97
Q13.15
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.43

Descriptive statistics

Standard deviation0.2681658264
Coefficient of variation (CV)0.08053749773
Kurtosis-0.8017255526
Mean3.329701493
Median Absolute Deviation (MAD)0.23
Skewness-0.02164494186
Sum669.27
Variance0.07191291045
2021-06-23T10:39:05.835148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.62 23 11.4%
 
3.19 20 10.0%
 
3.15 15 7.5%
 
2.97 12 6.0%
 
3.03 10 5.0%
 
3.46 9 4.5%
 
3.78 8 4.0%
 
3.43 8 4.0%
 
3.31 8 4.0%
 
2.91 7 3.5%
 
Other values (29) 81 40.3%
 
ValueCountFrequency (%) 
2.54 1 0.5%
 
2.68 1 0.5%
 
2.91 7 3.5%
 
2.92 1 0.5%
 
2.97 12 6.0%
 
ValueCountFrequency (%) 
3.94 1 0.5%
 
3.8 2 1.0%
 
3.78 8 4.0%
 
3.76 1 0.5%
 
3.74 3 1.5%
 

stroke
Real number (ℝ≥0)

Distinct count36
Unique (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2617412935323378
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:06.175149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.46
95-th percentile3.64
Maximum4.17
Range2.1
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.3178749011
Coefficient of variation (CV)0.09745558353
Kurtosis2.062831978
Mean3.261741294
Median Absolute Deviation (MAD)0.17
Skewness-0.7257486844
Sum655.61
Variance0.1010444527
2021-06-23T10:39:06.516148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.4 19 9.5%
 
3.23 14 7.0%
 
3.15 14 7.0%
 
3.03 14 7.0%
 
3.39 13 6.5%
 
2.64 11 5.5%
 
3.5 10 5.0%
 
3.35 9 4.5%
 
3.29 9 4.5%
 
3.46 8 4.0%
 
Other values (26) 80 39.8%
 
ValueCountFrequency (%) 
2.07 1 0.5%
 
2.19 2 1.0%
 
2.36 1 0.5%
 
2.64 11 5.5%
 
2.68 2 1.0%
 
ValueCountFrequency (%) 
4.17 2 1.0%
 
3.9 3 1.5%
 
3.86 4 2.0%
 
3.64 5 2.5%
 
3.58 6 3.0%
 

compression_ratio
Real number (ℝ≥0)

Distinct count32
Unique (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.164278606965174
Minimum7.0
Maximum23.0
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:06.833149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.5
Q18.6
median9
Q39.4
95-th percentile21.9
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.004965493
Coefficient of variation (CV)0.3940235848
Kurtosis5.068872476
Mean10.16427861
Median Absolute Deviation (MAD)0.4
Skewness2.584462433
Sum2043.02
Variance16.0397486
2021-06-23T10:39:07.198151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9 46 22.9%
 
9.4 26 12.9%
 
8.5 14 7.0%
 
9.5 13 6.5%
 
9.3 11 5.5%
 
8.7 9 4.5%
 
8 8 4.0%
 
9.2 8 4.0%
 
7 6 3.0%
 
23 5 2.5%
 
Other values (22) 55 27.4%
 
ValueCountFrequency (%) 
7 6 3.0%
 
7.5 5 2.5%
 
7.6 4 2.0%
 
7.7 2 1.0%
 
7.8 1 0.5%
 
ValueCountFrequency (%) 
23 5 2.5%
 
22.7 1 0.5%
 
22.5 3 1.5%
 
22 1 0.5%
 
21.9 1 0.5%
 

horsepower
Real number (ℝ≥0)

Distinct count58
Unique (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.2636815920398
Minimum48
Maximum262
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:07.556811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile176
Maximum262
Range214
Interquartile range (IQR)46

Descriptive statistics

Standard deviation37.38937181
Coefficient of variation (CV)0.3620766879
Kurtosis1.327302314
Mean103.2636816
Median Absolute Deviation (MAD)25
Skewness1.155371578
Sum20756
Variance1397.965124
2021-06-23T10:39:07.884809image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
68 19 9.5%
 
69 10 5.0%
 
116 9 4.5%
 
70 9 4.5%
 
110 8 4.0%
 
95 7 3.5%
 
114 6 3.0%
 
62 6 3.0%
 
101 6 3.0%
 
88 6 3.0%
 
Other values (48) 115 57.2%
 
ValueCountFrequency (%) 
48 1 0.5%
 
52 2 1.0%
 
55 1 0.5%
 
56 2 1.0%
 
58 1 0.5%
 
ValueCountFrequency (%) 
262 1 0.5%
 
207 3 1.5%
 
200 1 0.5%
 
184 2 1.0%
 
182 3 1.5%
 

peak_rpm
Real number (ℝ≥0)

Distinct count22
Unique (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5121.393034825871
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:08.204811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4250
Q14800
median5200
Q35500
95-th percentile6000
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation479.6249053
Coefficient of variation (CV)0.09365125895
Kurtosis0.06898831901
Mean5121.393035
Median Absolute Deviation (MAD)300
Skewness0.08832001944
Sum1029400
Variance230040.0498
2021-06-23T10:39:08.544807image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5500 38 18.9%
 
4800 36 17.9%
 
5000 27 13.4%
 
5200 23 11.4%
 
5400 11 5.5%
 
6000 9 4.5%
 
5800 7 3.5%
 
4500 7 3.5%
 
5250 7 3.5%
 
4150 5 2.5%
 
Other values (12) 31 15.4%
 
ValueCountFrequency (%) 
4150 5 2.5%
 
4200 5 2.5%
 
4250 3 1.5%
 
4350 4 2.0%
 
4400 3 1.5%
 
ValueCountFrequency (%) 
6600 2 1.0%
 
6000 9 4.5%
 
5900 3 1.5%
 
5800 7 3.5%
 
5600 1 0.5%
 

city_mpg
Real number (ℝ≥0)

HIGH CORRELATION
Distinct count29
Unique (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.17910447761194
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:08.879729image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119
median24
Q330
95-th percentile37
Maximum49
Range36
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.423220469
Coefficient of variation (CV)0.2551012279
Kurtosis0.7539680878
Mean25.17910448
Median Absolute Deviation (MAD)5
Skewness0.6804334707
Sum5061
Variance41.25776119
2021-06-23T10:39:09.242730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
31 28 13.9%
 
19 27 13.4%
 
24 22 10.9%
 
27 14 7.0%
 
17 12 6.0%
 
23 12 6.0%
 
26 12 6.0%
 
21 8 4.0%
 
25 8 4.0%
 
30 8 4.0%
 
Other values (19) 50 24.9%
 
ValueCountFrequency (%) 
13 1 0.5%
 
14 2 1.0%
 
15 3 1.5%
 
16 5 2.5%
 
17 12 6.0%
 
ValueCountFrequency (%) 
49 1 0.5%
 
47 1 0.5%
 
45 1 0.5%
 
38 5 2.5%
 
37 6 3.0%
 

highway_mpg
Real number (ℝ≥0)

HIGH CORRELATION
Distinct count30
Unique (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.686567164179106
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:09.569772image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median30
Q334
95-th percentile42
Maximum54
Range38
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.815149936
Coefficient of variation (CV)0.22208903
Kurtosis0.5611711398
Mean30.68656716
Median Absolute Deviation (MAD)5
Skewness0.5495071459
Sum6168
Variance46.44626866
2021-06-23T10:39:09.909774image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25 19 9.5%
 
38 17 8.5%
 
24 17 8.5%
 
30 16 8.0%
 
32 16 8.0%
 
34 14 7.0%
 
37 13 6.5%
 
28 12 6.0%
 
29 10 5.0%
 
33 9 4.5%
 
Other values (20) 58 28.9%
 
ValueCountFrequency (%) 
16 2 1.0%
 
17 1 0.5%
 
18 2 1.0%
 
19 2 1.0%
 
20 2 1.0%
 
ValueCountFrequency (%) 
54 1 0.5%
 
53 1 0.5%
 
50 1 0.5%
 
47 2 1.0%
 
46 2 1.0%
 

price
Real number (ℝ≥0)

Distinct count186
Unique (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13207.129353233831
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Memory size1.7 KiB
2021-06-23T10:39:10.243776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6189
Q17775
median10295
Q316500
95-th percentile32528
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation7947.066342
Coefficient of variation (CV)0.601725487
Kurtosis3.231536887
Mean13207.12935
Median Absolute Deviation (MAD)3306
Skewness1.809675339
Sum2654633
Variance63155863.44
2021-06-23T10:39:10.601872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
13499 2 1.0%
 
16500 2 1.0%
 
9279 2 1.0%
 
7609 2 1.0%
 
6229 2 1.0%
 
8495 2 1.0%
 
18150 2 1.0%
 
8921 2 1.0%
 
7898 2 1.0%
 
6692 2 1.0%
 
Other values (176) 181 90.0%
 
ValueCountFrequency (%) 
5118 1 0.5%
 
5151 1 0.5%
 
5195 1 0.5%
 
5348 1 0.5%
 
5389 1 0.5%
 
ValueCountFrequency (%) 
45400 1 0.5%
 
41315 1 0.5%
 
40960 1 0.5%
 
37028 1 0.5%
 
36880 1 0.5%
 

Interactions

2021-06-23T10:37:22.464114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:22.843500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:23.177498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:23.501503image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:23.832491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:24.170501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:24.510502image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:24.846697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:25.175697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:25.510691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:25.837697image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:26.169694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:26.498696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:26.995726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:27.370726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:27.707726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:28.059726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:28.400727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:28.735568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:29.113568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:29.446568image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:29.791566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:30.133569image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:30.472566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:30.816973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:31.150976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:31.495973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:31.854972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:32.200972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:32.545970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:32.886732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:33.237732image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:33.587735image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:33.930497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:34.283499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:34.619498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:35.146740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:35.476744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:35.826744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:36.170741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:36.497744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:36.854416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:37.182417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:37.536418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:37.868418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:38.210420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:38.565417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:38.911654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:39.299656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:39.629654image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:39.996648image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:40.340657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:40.688656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:41.063345image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:41.404341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:41.737350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:42.086342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:42.421347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:42.757348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:43.124148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:43.533144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:43.987978image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:44.383981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:44.752983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:45.222662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:45.873662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:46.228662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:46.560660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:46.901403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:47.263400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:47.587403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:47.933399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:48.266401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:48.609403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:48.954743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:49.293747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:49.630743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:49.949745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:50.312742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:50.658747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:51.009499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:51.343493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:51.697497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:52.084494image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:52.445499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:52.803497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:53.156064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:53.488064image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:53.832817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:54.176820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:54.566821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:54.912074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:55.263077image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:55.599073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:55.948074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:56.289073image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:56.642075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:56.997302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:57.343312image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:57.706313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:58.040314image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:58.637317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:58.989436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:59.352441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:37:59.688445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:00.045444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:00.379435image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:00.748441image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:01.114152image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:01.431154image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:01.769150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:02.111153image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:02.469156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:02.802150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:03.140117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:03.494116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:03.843799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:04.195808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:04.525808image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:04.886812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:05.213075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:05.554076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:05.972080image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:06.423072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:06.849070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:07.195616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:07.547617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:07.888615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:08.226619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:08.569617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:08.917818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:09.270817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:09.630821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:09.964814image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:10.321771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:10.655780image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:11.029690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:11.399688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:11.729691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:12.091691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:12.437685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:12.774688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:13.149410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:13.518413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:13.894984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:14.224974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:14.573983image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:15.289564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:15.646562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:15.981560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:16.333562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:16.652562image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:16.986200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:17.345216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:17.664212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:18.027215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:18.352216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:18.695212image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:19.085095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:19.436100image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:19.791103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:20.141101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:20.495103image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:20.839916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:21.205658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:21.555660image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:21.909662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:22.244647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:22.602659image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:22.958658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:23.329642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:23.708640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:24.132666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:24.510679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:24.862678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:25.210948image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:25.565947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:25.881949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:26.205947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:26.541950image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:26.870951image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:27.223460image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:27.538465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:27.879463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:28.214463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:28.539463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:28.871402image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:29.204401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:29.529405image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:29.857400image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:30.186399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:30.540404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:30.877404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:31.199615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:31.544615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:31.870616image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:32.224620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:32.574615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:32.907617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:33.240926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:33.565925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:33.903875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:34.237877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:34.563877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:34.892874image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:35.648327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:35.970336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:36.316326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:36.662327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:37.002328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:37.374324image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:37.741325image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:38.084323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:38.436323image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:38.787186image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:39.126619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:39.474618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:39.820617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:40.149617image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:40.505615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:40.832614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:41.180166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:41.521168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:41.874167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:42.228166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:42.583169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:42.940163image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:43.310663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:43.689664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:44.046529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:44.389529image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:44.741531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:45.108129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:45.466389image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:45.867393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:46.224394image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:46.564393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:46.909393image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:47.301979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:47.650979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:47.995980image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:48.350981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:48.700981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:49.058776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:49.432028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:49.783027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:50.140025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:50.479026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:50.808029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:51.158922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:51.496381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:51.852382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:52.217382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:52.572374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:52.913381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:53.291799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-23T10:39:11.052868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-23T10:39:11.608337image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-23T10:39:12.143338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-23T10:39:12.798338image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-23T10:39:13.391749image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-23T10:38:53.980504image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-23T10:38:54.981510image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Sample

First rows

symbolingnormalized_lossesmakefuel_typeaspirationnumber_of_doorsbody_styledrive_wheelsengine_locationwheel_baselengthwidthheightcurb_weightengine_typenumber_of_cylindersengine_sizefuel_systemborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgprice
03168alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212713495
13168alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.01115000212716500
21168alfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.01545000192616500
32164audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.01025500243013950
42164audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.01155500182217450
52161audigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.51105500192515250
61158audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.51105500192517710
71168audigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.51105500192518920
81158audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.31405500172023875
92192bmwgasstdtwosedanrwdfront101.2176.864.854.32395ohcfour108mpfi3.502.808.81015800232916430

Last rows

symbolingnormalized_lossesmakefuel_typeaspirationnumber_of_doorsbody_styledrive_wheelsengine_locationwheel_baselengthwidthheightcurb_weightengine_typenumber_of_cylindersengine_sizefuel_systemborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgprice
191-174volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.51145400232813415
192-2103volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.51145400242815985
193-174volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.51145400242816515
194-2103volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.51625100172218420
195-174volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.51625100172218950
196-195volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.51145400232816845
197-195volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.71605300192519045
198-195volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.81345500182321485
199-195volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.01064800262722470
200-195volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.51145400192522625